Accident; analysis and prevention
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This study identified contributing factors in the occurrence of motor vehicle crashes (MVCs) and the severity of crashes according to work-related status in Utah. Analyses were based on probabilistically linked data involving police crash reports and hospital inpatient and emergency department (ED) records for the years 1999-2005. Of 643,647 drivers involved in crashes, 73,437 (11.4%) went to the emergency department (ED) and 4989 (0.8%) were hospitalized. ⋯ Of those attending the ED because of a crash, workers were significantly more likely to have broken bones, bleeding wounds, or to die. Of those hospitalized because of a crash, workers were significantly less likely to have caused the crash (65% [145/223] vs. 73% [3,315/4,566], P<0.001). Yet although those drivers who were working at the time of the crash compared with those not working were less likely to have alcohol involved or to have caused the crash, there remains room for improvement among workers with respect to these factors, as well as safety belt use and fatigue.
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The focus of this paper is twofold: (1) to examine the non-linear relationship between pedestrian crashes and predictor variables such as demographic characteristics (population and household units), socio-economic characteristics (mean income and total employment), land use characteristics, road network characteristics (the number of lanes, speed limit, presence of median, and pedestrian and vehicular volume) and accessibility to public transit systems, and (2) to develop generalized linear pedestrian crash estimation models (based on negative binomial distribution to accommodate for over-dispersion of data) by the level of pedestrian activity and spatial proximity to extract site specific data at signalized intersections. Data for 176 randomly selected signalized intersections in the City of Charlotte, North Carolina were used to examine the non-linear relationships and develop pedestrian crash estimation models. ⋯ Models were then developed separately for all signalized intersections, high pedestrian activity signalized intersections and low pedestrian activity signalized intersections. The use of 0.25mile, 0.5mile and 1mile buffer widths to extract data and develop models was also evaluated.
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A multivariate logistic regression model, based upon National Automotive Sampling System Crashworthiness Data System (NASS-CDS) data for calendar years 1999-2008, was developed to predict the probability that a crash-involved vehicle will contain one or more occupants with serious or incapacitating injuries. These vehicles were defined as containing at least one occupant coded with an Injury Severity Score (ISS) of greater than or equal to 15, in planar, non-rollover crash events involving Model Year 2000 and newer cars, light trucks, and vans. The target injury outcome measure was developed by the Centers for Disease Control and Prevention (CDC)-led National Expert Panel on Field Triage in their recent revision of the Field Triage Decision Scheme (American College of Surgeons, 2006). ⋯ The area under the receiver operator characteristic (ROC) curve for the final model was 0.84. Delta-V (mph), seat belt use and crash direction were the most important predictors of serious injury. Due to the complexity of factors associated with rollover-related injuries, a separate screening algorithm is needed to model injuries associated with this crash mode.
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This study examined the correlates of injury severity using police records of pedestrian-motor-vehicle collisions on state routes and city streets in King County, Washington. Levels of influence on collision outcome considered (1) the characteristics of individual pedestrians and drivers and their actions; (2) the road environment; and (3) the neighborhood environment. Binary logistic regressions served to estimate the risk of a pedestrian being severely injured or dying versus suffering minor or no injury. ⋯ Road intersection design was significant only in the state route models, with pedestrians crossing at intersections without signals increasing the risk of being injured or dying. Adjusting for pedestrians' and drivers' characteristics and actions, neighborhood medium home values and higher residential densities increased the risk of injury or death. No other road or neighborhood environment variable remained significant, suggesting that pedestrians were not safer in areas with high pedestrian activity.
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We developed a hazard perception test, modeled on that used currently in several Australian states, that presents short video scenes to observers and requires them to indicate the presence of a traffic conflict that would lead to a collision between the "camera" vehicle and another road user. After eliminating those scenes that were problematic (e.g., many observers did not recognize the hazard), we predicted driver group (novice vs. experienced drivers of similar age) on the basis of individual differences in reaction time, miss rate and false alarm rate. Novices were significantly slower in responding to hazards, even after controlling for age and simple reaction time. ⋯ There was good reliability in the resulting scale. Results suggest that this brief test of hazard perception can discriminate groups that differ in driving experience. Implications for driver licensing, evaluation and training are discussed.